Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.
Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.
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Netpyne的体感丘脑皮层电路的大规模生物物理详细模型。
DOI：
10.3389/fninf.2022.884245
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发表时间：
2022
影响因子：
3.5
通讯作者：
Dura-Bernal, Salvador
中科院分区：
文献类型：
--
作者：
Borges, Fernando S.;Moreira, Joao V. S.;Takarabe, Lavinia M.;Lytton, William W.;Dura-Bernal, Salvador
哺乳动物的主要体感皮质（S1）在感知和相关的感觉运动行为中至关重要。 2015年，Blue Brain Project（BBP）开发了一种开创性的大鼠S1微电路模拟，具有超过31,000个神经元，具有207种形态电动神经元类型和3700万突触，并结合了来自广泛实验性研究的解剖学和生理信息。我们使用新皮层微电路协作门户网站中可用的数据在Netpyne中实现了这种高度详细且复杂的S1模型。 Netpyne为神经元提供了Python高级接口，并允许使用直观的声明性标准化语言来定义复杂的多尺度模型。它还可以促进运行并行模拟，自动使用超级计算机对参数的优化和探索，并提供广泛的内置分析功能。这将使S1模型更容易访问，更简单地扩展，修改和扩展，以探索研究问题或与其他现有模型互连。尽管有一些实施差异，但Netpyne模型仍保留了所有207个细胞类型的原始细胞形态，电生理反应和空间分布。以及所有1941年途径的连通性特性，包括突触动力学和短期可塑性（STP）。 Netpyne S1模拟在所有人群中产生了合理的生理发射率和活动模式。当包括STP时，网络产生的1 Hz振荡与体外样状态的原始模型相当。到那时，该模型降低了细胞外钙的浓度，该模型以异步活性在体内样状态下重现了原始的S1。这些结果使用新的建模工具验证了原始研究。模拟的局部场电位（LFP）表现出现实的振荡模式和特征，包括距离和频率依赖性衰减。该模型是通过添加丘脑电路来扩展的，其中包括6个不同的丘脑种群，这些丘脑群，丘脑，丘脑皮质（TC）和皮质丘脑连通性来自实验数据。丘脑模型再现了单个已知的细胞和电路级动力学，包括爆发和滋补模式以及振荡模式，为皮质提供了更现实的输入，并使对TC相互作用的研究提供了研究。总体而言，我们的工作提供了体感TC电路的广泛访问，数据驱动和生物物理的详细模型，可作为研究人员研究神经动态，功能和疾病的社区工具。
The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project (BBP) developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. When STP was included, the network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical (TC) and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of TC interactions. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory TC circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.
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细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
3.5
作者：
Ecker A;Romani A;Sáray S;Káli S;Migliore M;Falck J;Lange S;Mercer A;Thomson AM;Muller E;Reimann MW;Ramaswamy S
通讯作者：
Ramaswamy S
影响因子：
15.9
作者：
Barthas F;Kwan AC
通讯作者：
Kwan AC
影响因子：
4.3
作者：
Arkhipov, Anton;Gouwens, Nathan W.;Koch, Christof
通讯作者：
Koch, Christof
影响因子：
3.5
作者：
Hagen, Espen;Naess, Solveig;Einevoll, Gaute T.
通讯作者：
Einevoll, Gaute T.